SynthesisBritish journal of clinical pharmacology2026
Accelerating tacrolimus model-informed precision dosing in kidney transplant recipients: Model evaluation and refinement strategies.
Synthesis in British journal of clinical pharmacology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Accelerating tacrolimus model-informed precision dosing in kidney transplant recipients: Model evaluation and refinement strategies.British journal of clinical pharmacology · 2026Pooled it
- Model-Informed Precision Dosing of Tacrolimus Initial Dosage Optimization in Membranous Nephropathy Patients.Drug design, development and therapy · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
aimTo guide the efficient implementation of Model-Informed Precision Dosing (MIPD) by comparing the predictive performance of published tacrolimus population pharmacokinetic (popPK) models with an in-house model in Uruguayan kidney transplant recipients, using comprehensive external evaluation methodology.
methodsTwenty-five adult tacrolimus popPK models were identified through systematic review and externally evaluated using retrospective data from 39 patients (396 steady-state concentrations). In parallel, an in-house model was developed with prospective and retrospective data from 49 patients (727 concentrations). Predictive performance was assessed across different data integration levels: a priori predictions and sequential Bayesian forecasting with progressive incorporation of individual observations.
resultsAll published models and the in-house model showed poor a priori predictive performance, with even the best performing models yielding MAPE values near 50% and fewer than 30% of predictions within 30% of observed concentrations. Bayesian forecasting progressively improved bias, accuracy and dispersion of prediction errors. Initial incorporation of three trough concentrations produced limited benefit, but substantial improvements were observed upon integration of a pharmacokinetic curve composed of at least 3 post-dose concentrations, at which point several published models and the in-house model achieved high precision, with mean bias below 10% and MAPE below 30%. Two published models exhibited comparable performance to the in-house model, which achieved marginally better results as Bayesian forecasting incorporated successive individual observations, without providing a meaningful overall advantage.
conclusionPublished tacrolimus popPK models can achieve clinically reliable predictions when combined with Bayesian updating, performing comparably to in-house developed models. These findings support a resource-efficient strategy for implementing tacrolimus MIPD without the need for local model development, provided that a pharmacokinetic curve is obtained early post-transplantation.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.